Unravelling the brain networks driving spike-wave discharges in genetic generalized epilepsy-common patterns and individual differences

Comparison of Resting-State EEG Network Analyses With and Without Parallel MRI in Genetic Generalized Epilepsy

Genetic generalized epilepsy (GGE) is conceptualized as a brain disorder involving distributed bilateral networks. To study these networks, simultaneous EEG-fMRI measurements can be used. However, inside-MRI EEG suffers from strong MR-related artifacts; it is not established whether EEG-based metrics in EEG-fMRI resting-state measurements are suitable for the analysis of group differences at source-level. We evaluated the impact of the inside-MR measurement condition on statistical group comparisons of EEG on source-level power and functional connectivity in patients with GGE versus healthy controls. We studied the cross-modal spatial relation of statistical group differences in seed-based FC derived from EEG and parallel fMRI. We found a significant increase in power and a frequency-specific change in functional connectivity for the inside MR-scanner compared to the outside MR-scanner condition. For power, we found reduced group difference between GGE and controls both in terms of statistical significance as well as effect size. Group differences for ImCoh remained similar both in terms of statistical significance as well as effect size. We found increased seed-based FC for GGE patients from the thalamus to the precuneus cortex region in fMRI, and in the theta band of simultaneous EEG. Our findings suggest that the analysis of EEG functional connectivity based on ImCoh is suitable for MR-EEG, and that relative group difference in a comparison of patients with GGE against controls are preserved. Spatial correspondence of seed-based FC group differences between the two modalities was found for the thalamus.

Abnormal static and dynamic functional network connectivity of the whole-brain in children with generalized tonic-clonic seizures

Introduction

Generalized tonic-clonic seizures (GTCS) are a subtype of generalized seizures exhibiting bursts of bilaterally synchronous generalized spike-wave discharges. Numerous neuroimaging studies have reported aberrant functional activity and topological organization of brain network in epilepsy patients with GTCS, but most studies have focused on adults. However, the effect of GTCS on the spatial and temporal properties of brain function in children remains unclear. The present study aimed to explore whole-brain static (sFC) and dynamic functional connectivity (dFC) in children with GTCS.

Methods

Twenty-three children with GTCS and 32 matched healthy controls (HCs) were recruited for the present study. Resting-state functional magnetic resonance imaging (MRI) data were collected for each subject. The group independent component analysis method was used to obtain independent components (ICs). Then, sFC and dFC methods were applied and the differences in functional connectivity (FC) were compared between the children with GTCS and the HCs. Additionally, we investigated the correlations between the dFC indicators and epilepsy duration.

Results

Compared to HCs, GTCS patients exhibited a significant decrease in sFC strengths among most networks. The K-means clustering method was implemented for dFC analysis, and the optimal number of clusters was estimated: two discrete connectivity configurations, State 1 (strong connection) and State 2 (weak connection). The decreased dFC mainly occurred in State 1, especially the dFC between the visual network (VIS) and somatomotor network (SMN); but the increased dFC mainly occurred in State 2 among most networks in GTCS children. In addition, GTCS children showed significantly shorter mean dwell time and lower fractional windows in stronger connected State 1, while GTCS children showed significantly longer mean dwell time in weaker connected State 2. In addition, the dFC properties, including mean dwell time and fractional windows, were significantly correlated with epilepsy duration.

Conclusion

Our results indicated that GTCS epilepsy not only alters the connectivity strength but also changes the temporal properties of connectivity in networks in the whole brain. These findings also emphasized the differences in sFC and dFC in children with GTCS. Combining sFC and dFC methods may provide more comprehensive understanding of the abnormal changes in brain architecture in children with GTCS.

EEG Network Analysis in Epilepsy with Generalized Tonic–Clonic Seizures Alone

Many contradictory theories regarding epileptogenesis in idiopathic generalized epilepsy have been proposed. This study aims to define the network that takes part in the formation of the spike-wave discharges in patients with generalized tonic–clonic seizures alone (GTCSa) and elucidate the network characteristics. Furthermore, we intend to define the most influential brain areas and clarify the connectivity pattern among them. The data were collected from 23 patients with GTCSa utilizing low-density electroencephalogram (EEG). The source localization of generalized spike-wave discharges (GSWDs) was conducted using the Standardized low-resolution brain electromagnetic tomography (sLORETA) methodology. Cortical connectivity was calculated utilizing the imaginary part of coherence. The network characteristics were investigated through small-world propensity and the integrated value of influence (IVI). Source localization analysis estimated that most sources of GSWDs were in the superior frontal gyrus and anterior cingulate. Graph theory analysis revealed that epileptic sources created a network that tended to be regularized during generalized spike-wave activity. The IVI analysis concluded that the most influential nodes were the left insular gyrus and the left inferior parietal gyrus at 3 and 4 Hz, respectively. In conclusion, some nodes acted mainly as generators of GSWDs and others as influential ones across the whole network.

Discerning Seizure-Onset v. Propagation Zone: Pre-and-Post-Operative Resting-State fMRI Directionality and Boerwinkle Neuroplasticity Index

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Directionality differentiates the SOZ and pZ with 88% accuracy, 73% specificity, and 95% sensitivity.

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The Boerwinkle Neuroplasticity Index >70% is predictive of good outcomes.

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Signal from seizure onset zone to propagation zone is excitatory and signal from propagation zone to seizure onset zone is inhibitory.

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Greater inhibition from propagation zone is associated with better surgical outcome.

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Pre to post-operative SOZ and pZ modulation was diminished as expected.

The goal of this study was to determine resting state fMRI (rs-fMRI) effective connectivity (RSEC) capacity, agnostic of epileptogenic events, in distinguishing seizure onset zones (SOZ) from propagation zones (pZ). Consecutive patients (2.1–18.2 years old), with epilepsy and hypothalamic hamartoma, pre-operative rs-fMRI-directed surgery, post-operative imaging, and Engel class I outcomes were collected. Cross-spectral dynamic causal modelling (DCM) was used to estimate RSEC between the ablated rs-fMRI-SOZ to its region of highest connectivity outside the HH, defined as the propagation zone (pZ). Pre-operatively, RSEC from the SOZ and PZ was expected to be positive (excitatory), and pZ to SOZ negative (inhibitory), and post-operatively to be either diminished or non-existent. Sensitivity, accuracy, positive predictive value were determined for node-to-node connections. A Parametric Empirical Bayes (PEB) group analysis on pre-operative data was performed to identify group effects and effects of Engel class outcome and age. Pre-operative RSEC strength was also evaluated for correlation with percent seizure frequency improvement, sex, and region of interest size. Of the SOZ’s RSEC, only 3.6% had no connection of significance to the pZ when patient models were individually reduced. Among remaining, 96% were in expected (excitatory signal found from SOZ → pZ and inhibitory signal found from pZ → SOZ) versus 3.6% reversed polarities. Both pre-operative polarity signals were equivalently as expected, with one false signal direction out of 26 each (3.7% total). Sensitivity of 95%, specificity 73%, accuracy of 88%, negative predictive value 88%, and positive predictive value of 88% in identifying and differentiating the SOZ and pZ. Groupwise PEB analysis confirmed SOZ → pZ EC was excitatory, and pZ → SOZ EC was inhibitory. Patients with better outcomes (Engel Ia vs. Ib) showed stronger inhibitory signal (pZ → SOZ). Age was negatively associated with absolute RSEC bidirectionally but had no relationship with Directionality SOZ identification performance. In an additional hierarchical PEB analysis identifying changes from pre-to-post surgery, SOZ → pZ modulation became less excitatory and pZ → SOZ modulation became less inhibitory. This study demonstrates the accuracy of Directionality to identify the origin of excitatory and inhibitory signal between the surgically confirmed SOZ and the region of hypothesized propagation zone in children with DRE due to a HH. Thus, this method validation study in a homogenous DRE population may have potential in narrowing the SOZ-candidates for epileptogenicity in other DRE populations and utility in other neurological disorders.

Neuroimaging and thalamic connectomics in epilepsy neuromodulation

Neuromodulation is an increasingly utilized therapy for the treatment of people with drug-resistant epilepsy. To date, the most common and effective target has been the thalamus, which is known to play a key role in multiple forms of epilepsy. Neuroimaging has facilitated rapid developments in the understanding of functional targets, surgical and programming techniques, and the effects of thalamic stimulation. In this review, the role of neuroimaging in neuromodulation is explored. First, the structural and functional changes of the thalamus in common epilepsy syndromes are discussed as the rationale for neuromodulation of the thalamus. Next, methods for imaging different thalamic nuclei are presented, as well as rationale for the need of direct surgical targeting rather than reliance on traditional stereotactic coordinates. Lastly, we discuss the potential role of neuroimaging in assessing the effects of thalamic stimulation and as a potential biomarker for neuromodulation outcomes.

Probabilistic mapping of thalamic nuclei and thalamocortical functional connectivity in idiopathic generalised epilepsy

It is well established that abnormal thalamocortical systems play an important role in the generation and maintenance of primary generalised seizures. However, it is currently unknown which thalamic nuclei and how nuclear‐specific thalamocortical functional connectivity are differentially impacted in patients with medically refractory and non‐refractory idiopathic generalised epilepsy (IGE). In the present study, we performed structural and resting‐state functional magnetic resonance imaging (MRI) in patients with refractory and non‐refractory IGE, segmented the thalamus into constituent nuclear regions using a probabilistic MRI segmentation method and determined thalamocortical functional connectivity using seed‐to‐voxel connectivity analyses. We report significant volume reduction of the left and right anterior thalamic nuclei only in patients with refractory IGE. Compared to healthy controls, patients with refractory and non‐refractory IGE had significant alterations of functional connectivity between the centromedian nucleus and cortex, but only patients with refractory IGE had altered cortical connectivity with the ventral lateral nuclear group. Patients with refractory IGE had significantly increased functional connectivity between the left and right ventral lateral posterior nuclei and cortical regions compared to patients with non‐refractory IGE. Cortical effects were predominantly located in the frontal lobe. Atrophy of the anterior thalamic nuclei and resting‐state functional hyperconnectivity between ventral lateral nuclei and cerebral cortex may be imaging markers of pharmacoresistance in patients with IGE. These structural and functional abnormalities fit well with the known importance of thalamocortical systems in the generation and maintenance of primary generalised seizures, and the increasing recognition of the importance of limbic pathways in IGE.

Dynamic Causal Modeling Self-Connectivity Findings in the Functional Magnetic Resonance Imaging Neuropsychiatric Literature

Dynamic causal modeling (DCM) is a method for analyzing functional magnetic resonance imaging (fMRI) and other functional neuroimaging data that provides information about directionality of connectivity between brain regions. A review of the neuropsychiatric fMRI DCM literature suggests that there may be a historical trend to under-report self-connectivity (within brain regions) compared to between brain region connectivity findings. These findings are an integral part of the neurologic model represented by DCM and serve an important neurobiological function in regulating excitatory and inhibitory activity between regions. We reviewed the literature on the topic as well as the past 13 years of available neuropsychiatric DCM literature to find an increasing (but still, perhaps, and inadequate) trend in reporting these results. The focus of this review is fMRI as the majority of published DCM studies utilized fMRI and the interpretation of the self-connectivity findings may vary across imaging methodologies. About 25% of articles published between 2007 and 2019 made any mention of self-connectivity findings. We recommend increased attention toward the inclusion and interpretation of self-connectivity findings in DCM analyses in the neuropsychiatric literature, particularly in forthcoming effective connectivity studies of substance use disorders.

fMRI-Based Effective Connectivity in Surgical Remediable Epilepsies: A Pilot Study

Simultaneous EEG-fMRI can contribute to identify the epileptogenic zone (EZ) in focal epilepsies. However, fMRI maps related to Interictal Epileptiform Discharges (IED) commonly show multiple regions of signal change rather than focal ones. Dynamic causal modeling (DCM) can estimate effective connectivity, i.e. the causal effects exerted by one brain region over another, based on fMRI data. Here, we employed DCM on fMRI data in 10 focal epilepsy patients with multiple IED-related regions of BOLD signal change, to test whether this approach can help the localization process of EZ. For each subject, a family of competing deterministic, plausible DCM models were constructed using IED as autonomous input at each node, one at time. The DCM findings were compared to the presurgical evaluation results and classified as: "Concordant" if the node identified by DCM matches the presumed focus, "Discordant" if the node is distant from the presumed focus, or "Inconclusive" (no statistically significant result). Furthermore, patients who subsequently underwent intracranial EEG recordings or surgery were considered as having an independent validation of DCM results. The effective connectivity focus identified using DCM was Concordant in 7 patients, Discordant in two cases and Inconclusive in one. In four of the 6 patients operated, the DCM findings were validated. Notably, the two Discordant and Invalidated results were found in patients with poor surgical outcome. Our findings provide preliminary evidence to support the applicability of DCM on fMRI data to investigate the epileptic networks in focal epilepsy and, particularly, to identify the EZ in complex cases.

Reduced structural covariance connectivity of defaut mode network and salience network in MRI-normal focal epilepsy

Neuroimaging studies have found altered functional connectivity of default mode network (DMN) and salience network (SN) in patients with focal epilepsy (FE). However, the structural basis underlying the functional connectivity disturbance in the patients is still unclear. Sixteen MRI-normal FE and 22 healthy controls were included in the current study. The T1 structural image of each participant was obtained. Seed-based structural covariance connectivity was employed to investigate changes of structural covariance connectivity of DMN and SN in FE patients. We further evaluated gray matter volume changes of brain areas showing altered structural connectivity in the patients. We found that patients with FE showed reduced connectivity of posterior cingulate cortex and left medial prefrontal cortex, hippocampus and orbitofrontal cortex, and reduced connectivity of right fronto-insula cortex with left insula, orbitofrontal cortex, opercum part of inferior frontal cortex and right medial prefrontal cortex compared with healthy controls. Moreover, those brain areas showing significant reduced structural covariance connectivity in patients with FE also had a loss of gray matter volume, indicating that reduced structural connectivity of DMN and SN might be associated with gray matter atrophy in the patients. Those results highlight the crucial role of DMN and SN in the pathology of patients with FE, and provided structural basis for the functional disturbance of the two networks in this disease.

Network Perspectives on Epilepsy Using EEG/MEG Source Connectivity

The evolution of EEG/MEG source connectivity is both, a promising, and controversial advance in the characterization of epileptic brain activity. In this narrative review we elucidate the potential of this technology to provide an intuitive view of the epileptic network at its origin, the different brain regions involved in the epilepsy, without the limitation of electrodes at the scalp level. Several studies have confirmed the added value of using source connectivity to localize the seizure onset zone and irritative zone or to quantify the propagation of epileptic activity over time. It has been shown in pilot studies that source connectivity has the potential to obtain prognostic correlates, to assist in the diagnosis of the epilepsy type even in the absence of visually noticeable epileptic activity in the EEG/MEG, and to predict treatment outcome. Nevertheless, prospective validation studies in large and heterogeneous patient cohorts are still lacking and are needed to bring these techniques into clinical use. Moreover, the methodological approach is challenging, with several poorly examined parameters that most likely impact the resulting network patterns. These fundamental challenges affect all potential applications of EEG/MEG source connectivity analysis, be it in a resting, spiking, or ictal state, and also its application to cognitive activation of the eloquent area in presurgical evaluation. However, such method can allow unique insights into physiological and pathological brain functions and have great potential in (clinical) neuroscience.